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[D] NEAT algorithms for generalized learning?

I was watching this video, and I actually love the concept of the NEAT algorithm for learning how to achieve goals through neural network evolution. My question is; If a network was trained on a particular level, would it be able to apply those same techniques to beat other levels, or is it just extremely fine-tuned to beat that one level?

Thinking about playing SMW when I was growing up, I died a lot, but after a while I got good enough at the game that I could beat levels I’d never played before in my first attempt. This means I would have built up the experience necessary to deal with unknown game mechanics, rapidly be able to learn how they work, and utilize them to achieve the end goal. Would networks trained via the NEAT algorithm be able to do the same thing? My initial thoughts are: no way…

submitted by /u/Chris_Hemsworth
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.